Much of the effort in improving the signal-to-noise ratio (SNR) of radio-frequency (RF) transmissions resides in the physical layer, i.e. antennas, cabling, and filters.
Digital signal processing methods (DSP) have also been used to boost SNR. Such DSP methods have included simple matched filtering, in which a time-reversed template signal is convolved with an unknown signal to locate the maximum correlation, or Fourier based filtering, in which certain spectral bands are suppressed to excise the signal. More complex methods can also be used, such as Bayesian decision rules to decide if a signal is present or not present (optimal detection theory). Sometimes, representing the signal in a different way, such as by projecting into a new space in which the signal and noise no longer overlap (via wavelet or other decompositions) can allow an improvement in SNR.
A crucial weakness of the standard methods of improving SNR is the reliance upon prior information in regard to the signal being denoised. Blind SNR improvement is much more challenging. Many signals have both high and low frequency components, and they are often mixed in complex ways. The same can be said of the environmental noise. Without a clear knowledge of the specific features of the signal and the noise, blind methods are very likely to filter out important features of the signal. Similarly, it is insufficient to have a known signal and unknown noise, or vice versa, as blind filtering is likely to have detrimental effects on the signal.
In view of the above, it would be desirable to be able to denoise signals in order to improve SNR, without relying upon prior information regarding the signals being denoised or the noise to which the signals are subjected.